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The future of aircraft ground operations - The case of passenger boarding
Abstract
This cumulative habilitation thesis documents my research in the field of air traffic management with a specific focus on the aircraft ground operations. My research interests aims at a seamless transport network, where the aircraft and passenger trajectories are synchronized. Thus, I started working on aircraft turnaround and in particular the passenger controlled boarding process. My research followed consequently a development process: model creation, calibration, validation, evaluation of technologies and procedures, and field trials. Finally, I developed and realized two innovative concepts: a future connected cabin, which allows to predict the boarding progress in real-time using the connected aircraft cabin as a sensor network, and the dynamic seat allocation, which provides a passenger and service focused boarding with a minimum of negative interferences.
I developed a stochastic model for aircraft boarding to cover both individual passenger behavior and operational constraints from airlines and airport. In close cooperation with airlines and airports field trials were conducted and field measurements were used to calibrate input parameters of the boarding model and to validate simulation results. The stochastic model was used to evaluate a high bandwidth of different boarding strategies and innovative technologies, such as the Side-Slip Seat. The evaluation shows that the numbers of expected interferences between passengers during storage of hand luggage and seating could be used as a metric of complexity to predict the final boarding time. In the next step, a sensor concept was developed to detect the position of passengers in the aircraft cabin (seat sensor, sensor floor in the aisle). This concept was realized in a closed cooperation with Eurowings and supported by Cologne Bonn airport, where an A319 was equipped with sensors in the cabin and a field trial was conducted. In parallel, the approach of dynamic seat allocation was successfully tested.
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Reliable and predictable ground operations are essential for punctual air traffic movements. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaround, which are mainly controlled by operational experts. Only the aircraft boarding, which is on the critical path of the turnaround, is driven by the passengers' experience and willingness or ability to follow the proposed procedures. We used a recurrent neural network approach to predict the progress of a running boarding event. In particular, we implemented and trained the Long Short-Term Memory model. Since no operational data of the specific passenger behavior is available, we used a reliable, validated boarding simulation environment to provide data about the aircraft boarding events. First predictions show that uni-variate input (seat load progress) produces insufficient results, so we consider expected passenger interactions in the aircraft cabin as well. These interactions are aggregated to a prior-developed complexity metric and allow an efficient evaluation of the current boarding progress. With this multi-variate input, our Long Short-Term Memory model achieves appropriate prediction results for the boarding progress.
Aircraft boarding is a process mainly impacted by the boarding sequence, individual passenger behavior and the amount of hand luggage. Whereas these aspects are widely addressed in scientific research and considered in operational improvements, the influence of infrastructural changes is only focused upon in the context of future aircraft design. The paper provides a comprehensive analysis of the innovative approach of a Side-Slip Seat, which allows passengers to pass each other during boarding. The seat holds the potential to reduce the boarding time by approx. 20%, even considering operational constraints, such as passenger conformance to the proposed boarding strategy. A validated stochastic boarding model is extended to analyze the impact of the Side-Slip Seat. The implementation of such fundamental change inside the aircraft cabin demands for adapted boarding strategies, in order to cover all the benefits that accompany this new dynamic seating approach. To reasonably identify efficient strategies, an evolutionary algorithm is used to systematically optimize boarding sequences. As a result, the evolutionary algorithm depicts that operationally relevant boarding strategies implementing the Side-Slip Seat should differentiate between the left and the right side of the aisle, instead of the current operationally preferred boarding from the back to the front.
The A-CDM's (Airport Collaborative Decision Making) goal of accurate turnaround time prediction in not met by traditional deterministic models or best guessing. During this research, the influence of the stochastic arrival process on the turnaround process is captured by modeling stochastically all elements of the turnaround as part of a Ground Manager (GMAN). Since arrival delay is one major factor influencing turnaround time process duration and variance, and delay occurs in larger amplitudes at U.S. airports compared to European ones due the absence of slot control, the presented GMAN concept is tested with arrival delay data from the U.S. NAS (National Airspace System). The arrival delay is collected from ASPM (Aviation System Performance Metrics) and custom probability distributions were fitted on the data for different airport categories. The results obtained from this analysis by showing the proof of concept of the GMAN, are discussed in this paper Turnaround, prediction, processes, buffers, delay, A-CDM
Insufficient ground operation performance with excessive process durations is one major contributor for flight delays caused on ground. Many European airports implemented first A-CDM but still need an automated ground process understanding and prediction system. Catching up our previous research activities we focused on explanations for turnaround process variations caused by staff skills. Therefore we analyzed the skills and the training of the ground handling staff in several steps, including a document analysis and interviews of different Ground Handlin companies within Germany. The analysis of public documents showed up a huge gap in the obligatory skills for turnaround staff. A slope of professional trained staff from airports with high traffic numbers to lower ones is also apparent. Additional we will show new findings in our research of delay impacts to process variations. As an outlook, all our efforts will approach in a complex Turnaround model predicting process duration for a satisfactory determination of the Turnaround Time and TOBT.
Efficient boarding procedures have to consider both operational constraints and the individual passenger behavior. In contrast to the aircraft handling processes of fueling, catering and cleaning, the boarding process is more driven by passengers than by airport or airline operators. This paper delivers a comprehensive set of operational data including classification of boarding times, passenger arrival times, times to store hand luggage, and passenger interactions in the aircraft cabin as a reliable basis for calibrating models for aircraft boarding. In this paper, a microscopic approach is used to model the passenger behavior, where the passenger movement is defined as a one-dimensional, stochastic, and time/space discrete transition process. This model is used to compare measurements from field trials of boarding procedures with simulation results and demonstrates a deviation smaller than 5%.
While the current turnaround handling shows potential for prediction and reliability improvement, the turnaround management approach (GMAN) of the Department of Air Traffic Technology and Logistics at TU Dresden describes a scientific foundation using a stochastic approach for process description and delay modeling. Based on recent air traffic network and delay analysis, new delay input data could be derived for Euro-pean airports. In a first step to integrate open and closed-loop process control for higher automation levels in turnaround management , the sub-processes of aircraft cleaning and boarding have been modeled and implemented, showing great potential of minimizing aircraft ground time in case of disturbances. Further enhancements to the turnaround model include the integration of the processes pushback and deicing, which by definition are not a part of the turnaround, but can significantly contribute to aircraft delay and therefore need to be considered for airport ground operations.
Future 4D aircraft trajectories demand comprehensive consideration of environmental, economic, and operational constraints, as well as reliable prediction of all aircraft-related processes. Mutual interdependencies between airports result in system-wide, far-reaching effects in the air traffic network (reactionary delays). To comply with airline/airport challenges over the day of operations, a change to an air-to-air perspective is necessary, with a specific focus on the aircraft ground operations as major driver for airline punctuality. Aircraft ground trajectories primarily consists of handling processes at the stand (deboarding, catering, fueling, cleaning, boarding, unloading, loading), which are defined as the aircraft turnaround. Turnaround processes are mainly controlled by ground handling, airport, or airline staff, except the aircraft boarding, which is driven by passengers' experience and willingness/ability to follow the proposed boarding procedures. This paper provides an overview of the research done in the field of aircraft boarding and introduces a reliable, calibrated, and stochastic aircraft boarding model. The stochastic boarding model is implemented in a simulation environment to evaluate specific boarding scenarios using different boarding strategies and innovative technologies. Furthermore, the potential of a connected aircraft cabin as sensor network is emphasized, which could provide information on the current and future status of the boarding process.
Common wisdom is that airplanes make money only when they are in the air. Therefore, turnaround time (turn time) on the ground should be reduced as much as possible. An important contribution to the turn time is airplane boarding time. Many different schemes are in use, from random seat selection to sophisticated boarding groups. A simulation model is described to evaluate different boarding strategies. In contrast to earlier work, it puts special emphasis on disturbances, such as a certain number of passengers not following their boarding group but boarding earlier or later. A surprising result of this work is that the typical back-to-front boarding strategy becomes improved when passengers do not board with their assigned group. Other proposed strategies still consist of small numbers of boarding groups but are both faster and more robust with regard to disturbances.
